Setup¶

Requirements¶

OptiWindNet has been tested on Windows 10/11 and on Linux systems, but should run on MacOSX as well.

Python version 3.11 or 3.12 is recommended to run OptiWindNet. Python 3.13+ may cause issues with the optiwindnet.db module, but all other features work fine. The last version to support Python 3.10 was v0.1.0.

Running OptiWindNet within a dedicated Python virtual environment is recommended. This can be achieved by installing either:

  • Python, which provides: venv virtual environment creator and pip package manager;

  • or Miniforge, which provides: conda environment and package manager.

Anaconda and Miniconda

Anaconda or Miniconda may be used to provide the conda manager, as long as the environment is configured to use the conda-forge channel.

Installation¶

The following commands must be run from the system’s command line interface (e.g. git-bash, cmd, powershell).

If using venv + pip¶

Create a new venv:

python -m venv optiwindnet_env

Activate optiwindnet_env (choose the one that matches your command prompt):

  • cmd: optiwindnet_env\Scripts\activate.bat

  • bash: source optiwindnet_env/Scripts/activate

  • powershell: optiwindnet_env\Scripts\Activate.ps1

And finally:

pip install optiwindnet

If using conda¶

conda create --name optiwindnet_env --channel conda-forge python=3.12 optiwindnet 
conda activate optiwindnet_env

The flag --channel conda-forge may be omitted if using miniforge or if the global conda configuration already sets conda-forge as the highest-priority channel.

Optional - Solvers¶

The installation procedure above enables OptiWindNet’s heuristics, meta-heuristic and mathematical optimization with Google’s OR-Tools (open-source software).

Other solvers can be used for mathematical optimization, but they are not installed by default.

The commands suggested here assume that the Python environment for OptiWindNet has been already activated and that conda is configured for the conda-forge channel. For packages that are installable with both pip and conda, enter only one of the commands.

Solvers perform a search across the branch-and-bound tree. This process can be accelerated in multi-core computers by using concurrent threads, but not all solvers have this feature. As of Dec/2025, only gurobi, cplex, cbc and fscip have this multi-threaded search capability.

Solvers ortools and scip also benefit from multi-core systems by launching multiple concurrent solvers in parallel, with some information exchange among them. ortools diversifies the algorithms/strategies among threads, while scip diversifies the random seeds and may use different emphasis among threads. Both have several user-configurable settings regarding that diversification.

For installing all pip-available solvers:

pip install optiwindnet[solvers]

See below for specific instructions for each solver.

Gurobi¶

Gurobi is proprietary software (academic license available). The trial version can only handle very small problems:

pip install gurobipy
conda install -c gurobi gurobi

CPLEX¶

IBM ILOG CPLEX is proprietary software (academic license available). The Community Edition version can only handle very small problems:

pip install cplex
conda install -c IBMDecisionOptimization cplex

HiGHS¶

HiGHS is open source software:

pip install highspy
conda install -c conda-forge highspy

CBC¶

COIN-OR’s Optimization Suite is open source software and its MILP solver is coin-or/Cbc: COIN-OR Branch-and-Cut solver.

Pyomo’s interface with CBC is through a system call, so it does not need to be part of a python environment, but Pyomo must be able to find the solver’s executable file. Conda has a package for CBC, but it may also be installed by following the instructions in the links above:

conda install -c conda-forge coin-or-cbc

SCIP¶

SCIP is open source software.

Attention: Avoid loading both scip and ortools solvers within the same Python interpreter instance, since ortools contains a SCIP library and its version may be different from the one used by pyscipopt.

pip install pyscipopt
conda install -c conda-forge pyscipopt

Note that these pyscipopt packages may not have been compiled with multi-threading capability. If you get the warning:

optiwindnet\MILP\scip.py:96: UserWarning: SCIP was compiled without task processing interface. Parallel solve not possible - using optimize() instead of solveConcurrent()
  model.solveConcurrent()

Then SCIP will still work, but will under-perform as it is limited to a single core. To overcome that, you will need to replace your current pyscipopt with a multi-threading-enabled one. PyPI-distributed pyscipopt version 6.0.0 is multi-threading-capable on all platforms (recommended). The one distributed via conda-forge is still at version 5.6.0 and is not multi-threading-capable.

FiberSCIP¶

FiberSCIP is a parallelized version of SCIP based on the Ubiquity Generator framework. It splits the branch-and-bound search tree among multiple SCIP threads (in a shared-memory system). It is different from SCIP’s concurrentSolve() in that each thread works on a different part of the tree, reducing duplicate work.

The 'fscip' solver in OptiWindNet is currently experimental, use at your own risk. The executable fscip must be reachable through the PATH environment. The pyscipopt package is required (see the SCIP section above), as well as a recent SCIP Optimization Suite (10.0.0+). Not all binary distributions of SCIPOptSuite include fscip, using one of the precompiled packages from that page is recommended.

Updating¶

Activate the Python environment for OptiWindNet and enter:

pip install --upgrade optiwindnet
conda update optiwindnet